CORE LOSSES CALCULATION PROBLEMS

At Frenetic we propose a new method for predicting ferrite properties. In this app note we will compare this new method with three other classical models for calculating hysteresis losses. These models have good accuracy in the frequency range from 50 kHz to 300 kHz, but have poor performance outside, mainly because of harmonics or closeness to saturation zone. In this application note, the results are shown.

At Frenetic we propose a new method for predicting ferrite properties. In this  app note we will compare this new method with three other classical models for  calculating hysteresis losses. These models have good accuracy in  the  frequency range from 50 kHz to 300 kHz, but have poor performance outside,  mainly because of harmonics or closeness to saturation zone. In this application  note, the results are shown.

Steinmetz

Improved General Steinmetz's  Equation. 

*Accurate Prediction of Ferrite Core Loss with Nonsinusoidal Waveforms  Using Only Steinmetz parameters. 

K. Venkatachalam C. R. Sullivan T. Abdallah H. Tacca

Pros: Coefficients provided by the  manufacturers. Easy implementation.

Cons: Inaccurate for waveforms with high  harmonic content. DC magnetization is not taken into  account.

Jiles-Atherton

Isotropic material model.

*Jiles D. C., Atherton D. "Theory of ferromagnetic hysteresis Journal of  Magnetism and Magnetic Materials 61 (1986) 48.

Pros: Easy implementation and quick  computing. Better than Steinmetz's with  harmonics.

Cons: Not all manufaturers give the parameters.Poor accuracy near saturation.

Preisach-Everett

Dynamic Preisach model.

*Preisach Type Hysteresis Models with Everett  Function in Closed Form, ZsoltvSzabó János Füzi

Pros: Good accuracy over a wide  frequency range. Works well with non-sinusoidal  waveforms.

Cons: Need to calculate the Preisach  coefficents and material parameters. Heavy computing.

Frenetic

Deep Learning Model.

Pros: Good accuracy over a wide  frequency range. Works well with non-sinusoidal waveforms.

Cons: Few data points given by  manufacturers.

Experimental results


CONCLUSIONS

The results given by the Artificial Intelligence keep the error rate low (~2%, much lower than the other models) due  to its intrinsic understanding of the non-linearities existing in the materials (in this case in a low-frequency range), in  addition to the blending of measurements and analytical models from which it learns. Predicting power losses with  such high accuracy enables engineers to optimize the design of magnetic components by letting them get closer to  the working limits of the materials, resulting in more compact and efficient power systems.

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